Multi-camera video surveillance : detection, occlusion handling, tracking and event recognition

Akman, Oytun
In this thesis, novel methods for background modeling, tracking, occlusion handling and event recognition via multi-camera configurations are presented. As the initial step, building blocks of typical single camera surveillance systems that are moving object detection, tracking and event recognition, are discussed and various widely accepted methods for these building blocks are tested to asses on their performance. Next, for the multi-camera surveillance systems, background modeling, occlusion handling, tracking and event recognition for two-camera configurations are examined. Various foreground detection methods are discussed and a background modeling algorithm, which is based on multi-variate mixture of Gaussians, is proposed. During occlusion handling studies, a novel method for segmenting the occluded objects is proposed, in which a top-view of the scene, free of occlusions, is generated from multi-view data. The experiments indicate that the occlusion handling algorithm operates successfully on various test data. A novel tracking method by using multi-camera configurations is also proposed. The main idea of multi-camera employment is fusing the 2D information coming from the cameras to obtain a 3D information for better occlusion handling and seamless tracking. The proposed algorithm is tested on different data sets and it shows clear improvement over single camera tracker. Finally, multi-camera trajectories of objects are classified by proposed multi-camera event recognition method. In this method, concatenated different view trajectories are used to train Gaussian Mixture Hidden Markov Models. The experimental results indicate an improvement for the multi-camera event recognition performance over the event recognition by using single camera.


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In this thesis, co-design and co-verification of a microcontroller hardware and software using SystemC is studied. For this purpose, an MC68HC11 microcontroller unit, a test bench that contains input and output modules for the verification of microcontroller unit are implemented using SystemC programming language and a visual simulation program is developed using C# programming language in Microsoft .NET platform. SystemC is a C++ class library that is used for co-designing hardware and software of a system...
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Image segmentation is an important research area in digital image processing with several applications in vision-guided autonomous robotics, product quality inspection, medical diagnosis, the analysis of remotely sensed images, etc. The aim of image segmentation can be defined as partitioning an image into homogeneous regions in terms of the features of pixels extracted from the image. Image segmentation methods can be classified into four main categories: 1) clustering methods, 2) region-based methods, 3) ...
Multi Camera Visual Surveillance for Motion Detection Occlusion Handling Tracking and Event Recognition
Akman, Oytun; Alatan, Abdullah Aydın; Çiloğlu, Tolga (null; 2008-10-05)
This paper presents novel approaches for background modeling, occlusion handling and event recognition by using multi-camera configurations that can be used to overcome the limitations of the single camera configurations. The main novelty in proposed background modeling approach is building multivariate Gaussians background model for each pixel of the reference camera by utilizing homography-related positions. Also, occlusion handling is achieved by generation of the top-view via trifocal tensors, as a resu...
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Citation Formats
O. Akman, “Multi-camera video surveillance : detection, occlusion handling, tracking and event recognition,” M.S. - Master of Science, Middle East Technical University, 2007.